Benchmarking reports

from sklearn_benchmarks.report import Reporting, ReportingHpo
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

reporting = Reporting(config_file_path="config.yml")
reporting.run()

Time report

	hour	min	sec
algo
KNeighborsClassifier	0.0	39.0	54.384087
daal4py_KNeighborsClassifier	0.0	2.0	30.456924
KNeighborsClassifier_kd_tree	0.0	2.0	35.909501
daal4py_KNeighborsClassifier_kd_tree	0.0	0.0	27.515845
KMeans_tall	0.0	0.0	21.163132
daal4py_KMeans_tall	0.0	0.0	8.017377
KMeans_short	0.0	0.0	2.684944
daal4py_KMeans_short	0.0	0.0	1.336918
LogisticRegression	0.0	0.0	18.183558
daal4py_LogisticRegression	0.0	0.0	3.792476
Ridge	0.0	0.0	10.171015
daal4py_Ridge	0.0	0.0	1.843034
HistGradientBoostingClassifier	0.0	5.0	8.638117
lightgbm	0.0	5.0	3.034249
xgboost	0.0	5.0	28.505828
catboost	0.0	5.0	0.341623
total	1.0	7.0	16.059444

KNeighborsClassifier vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	brute

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.165	0.000	4.835	0.000	1	5	NaN	NaN	0.468	0.000	0.354	0.000	See	See
1	KNeighborsClassifier	predict	1000000	1000	100	NaN	21.851	0.147	0.000	0.022	1	5	0.825	0.938	1.822	0.021	11.990	0.159	See	See
2	KNeighborsClassifier	predict	1000000	1	100	NaN	0.176	0.000	0.000	0.176	1	5	1.000	1.000	0.080	0.001	2.205	0.015	See	See
3	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.120	0.000	6.659	0.000	-1	100	NaN	NaN	0.451	0.000	0.266	0.000	See	See
4	KNeighborsClassifier	predict	1000000	1000	100	NaN	32.625	0.000	0.000	0.033	-1	100	0.930	0.827	1.775	0.018	18.377	0.188	See	See
5	KNeighborsClassifier	predict	1000000	1	100	NaN	0.156	0.014	0.000	0.156	-1	100	1.000	1.000	0.079	0.001	1.970	0.176	See	See
6	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.121	0.000	6.634	0.000	-1	1	NaN	NaN	0.451	0.000	0.267	0.000	See	See
7	KNeighborsClassifier	predict	1000000	1000	100	NaN	22.924	0.110	0.000	0.023	-1	1	0.715	0.717	1.803	0.027	12.712	0.202	See	See
8	KNeighborsClassifier	predict	1000000	1	100	NaN	0.154	0.016	0.000	0.154	-1	1	1.000	1.000	0.081	0.001	1.896	0.199	See	See
9	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.121	0.000	6.595	0.000	1	100	NaN	NaN	0.453	0.000	0.268	0.000	See	See
10	KNeighborsClassifier	predict	1000000	1000	100	NaN	22.191	0.063	0.000	0.022	1	100	0.930	0.827	1.784	0.015	12.438	0.111	See	See
11	KNeighborsClassifier	predict	1000000	1	100	NaN	0.177	0.000	0.000	0.177	1	100	1.000	1.000	0.080	0.000	2.214	0.012	See	See
12	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.118	0.000	6.762	0.000	-1	5	NaN	NaN	0.450	0.000	0.263	0.000	See	See
13	KNeighborsClassifier	predict	1000000	1000	100	NaN	32.879	0.000	0.000	0.033	-1	5	0.825	0.717	1.815	0.024	18.112	0.239	See	See
14	KNeighborsClassifier	predict	1000000	1	100	NaN	0.160	0.014	0.000	0.160	-1	5	1.000	1.000	0.080	0.001	1.995	0.180	See	See
15	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.117	0.000	6.820	0.000	1	1	NaN	NaN	0.449	0.000	0.261	0.000	See	See
16	KNeighborsClassifier	predict	1000000	1000	100	NaN	12.271	0.087	0.000	0.012	1	1	0.715	0.938	1.874	0.042	6.549	0.154	See	See
17	KNeighborsClassifier	predict	1000000	1	100	NaN	0.172	0.001	0.000	0.172	1	1	1.000	1.000	0.080	0.001	2.146	0.042	See	See
18	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.053	0.000	0.303	0.000	1	5	NaN	NaN	0.097	0.000	0.546	0.000	See	See
19	KNeighborsClassifier	predict	1000000	1000	2	NaN	17.844	0.023	0.000	0.018	1	5	0.977	0.986	0.323	0.005	55.331	0.831	See	See
20	KNeighborsClassifier	predict	1000000	1	2	NaN	0.024	0.000	0.000	0.024	1	5	1.000	1.000	0.006	0.000	4.340	0.147	See	See
21	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.053	0.000	0.304	0.000	-1	100	NaN	NaN	0.097	0.000	0.541	0.000	See	See
22	KNeighborsClassifier	predict	1000000	1000	2	NaN	28.692	0.138	0.000	0.029	-1	100	0.981	0.984	0.269	0.003	106.785	1.275	See	See
23	KNeighborsClassifier	predict	1000000	1	2	NaN	0.030	0.002	0.000	0.030	-1	100	1.000	1.000	0.005	0.000	5.830	0.396	See	See
24	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.054	0.000	0.294	0.000	-1	1	NaN	NaN	0.097	0.000	0.564	0.000	See	See
25	KNeighborsClassifier	predict	1000000	1000	2	NaN	20.473	0.075	0.000	0.020	-1	1	0.973	0.971	0.266	0.001	77.009	0.515	See	See
26	KNeighborsClassifier	predict	1000000	1	2	NaN	0.018	0.002	0.000	0.018	-1	1	1.000	1.000	0.005	0.000	3.621	0.452	See	See
27	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.061	0.000	0.263	0.000	1	100	NaN	NaN	0.096	0.000	0.635	0.000	See	See
28	KNeighborsClassifier	predict	1000000	1000	2	NaN	17.812	0.024	0.000	0.018	1	100	0.981	0.984	0.269	0.003	66.188	0.640	See	See
29	KNeighborsClassifier	predict	1000000	1	2	NaN	0.024	0.000	0.000	0.024	1	100	1.000	1.000	0.005	0.000	4.513	0.370	See	See
30	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.054	0.000	0.298	0.000	-1	5	NaN	NaN	0.097	0.000	0.552	0.000	See	See
31	KNeighborsClassifier	predict	1000000	1000	2	NaN	28.812	0.122	0.000	0.029	-1	5	0.977	0.971	0.264	0.002	109.002	1.087	See	See
32	KNeighborsClassifier	predict	1000000	1	2	NaN	0.029	0.001	0.000	0.029	-1	5	1.000	1.000	0.005	0.000	5.379	0.208	See	See
33	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.058	0.000	0.275	0.000	1	1	NaN	NaN	0.098	0.000	0.596	0.000	See	See
34	KNeighborsClassifier	predict	1000000	1000	2	NaN	9.584	0.027	0.000	0.010	1	1	0.973	0.986	0.316	0.003	30.323	0.257	See	See
35	KNeighborsClassifier	predict	1000000	1	2	NaN	0.015	0.000	0.000	0.015	1	1	1.000	1.000	0.005	0.000	2.766	0.113	See	See

KNeighborsClassifier_kd_tree vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	kd_tree

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.955	0.000	0.027	0.000	1	100	NaN	NaN	0.712	0.000	4.152	0.000	See	See
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	4.694	0.046	0.000	0.005	1	100	0.978	0.956	0.098	0.001	48.071	0.828	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.002	0.001	0.000	0.002	1	100	1.000	1.000	0.000	0.000	9.533	4.780	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.901	0.000	0.028	0.000	-1	100	NaN	NaN	0.749	0.000	3.872	0.000	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	2.737	0.021	0.000	0.003	-1	100	0.978	0.973	0.550	0.008	4.972	0.085	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.004	0.001	0.000	0.004	-1	100	1.000	1.000	0.001	0.000	5.153	1.995	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.906	0.000	0.028	0.000	-1	1	NaN	NaN	0.716	0.000	4.056	0.000	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.438	0.003	0.000	0.000	-1	1	0.959	0.975	0.182	0.002	2.402	0.035	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.002	0.000	0.000	0.002	-1	1	1.000	1.000	0.000	0.000	5.627	2.382	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.943	0.000	0.027	0.000	1	5	NaN	NaN	0.711	0.000	4.140	0.000	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	1.402	0.010	0.000	0.001	1	5	0.980	0.973	0.546	0.006	2.566	0.034	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.001	0.000	1.262	0.581	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.023	0.000	0.026	0.000	1	1	NaN	NaN	0.692	0.000	4.370	0.000	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.741	0.005	0.000	0.001	1	1	0.959	0.975	0.180	0.002	4.108	0.048	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	2.175	1.192	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.109	0.000	0.026	0.000	-1	5	NaN	NaN	0.655	0.000	4.749	0.000	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.837	0.007	0.000	0.001	-1	5	0.980	0.956	0.098	0.001	8.571	0.150	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.000	0.000	0.003	-1	5	1.000	1.000	0.000	0.000	10.150	4.234	See	See
18	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.792	0.000	0.020	0.000	1	100	NaN	NaN	0.460	0.000	1.722	0.000	See	See
19	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.053	0.000	0.000	0.000	1	100	0.985	0.977	0.001	0.000	70.901	22.598	See	See
20	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	100	1.000	1.000	0.000	0.000	5.923	4.719	See	See
21	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.776	0.000	0.021	0.000	-1	100	NaN	NaN	0.480	0.000	1.615	0.000	See	See
22	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.044	0.001	0.000	0.000	-1	100	0.985	0.990	0.007	0.000	6.449	0.499	See	See
23	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	100	1.000	1.000	0.000	0.000	18.825	14.080	See	See
24	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.700	0.000	0.023	0.000	-1	1	NaN	NaN	0.484	0.000	1.447	0.000	See	See
25	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.025	0.001	0.001	0.000	-1	1	0.972	0.990	0.001	0.000	21.860	5.912	See	See
26	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	1	1.000	1.000	0.000	0.000	19.111	14.763	See	See
27	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.701	0.000	0.023	0.000	1	5	NaN	NaN	0.441	0.000	1.588	0.000	See	See
28	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.023	0.000	0.001	0.000	1	5	0.985	0.990	0.006	0.000	3.633	0.254	See	See
29	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	4.744	3.779	See	See
30	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.711	0.000	0.023	0.000	1	1	NaN	NaN	0.451	0.000	1.575	0.000	See	See
31	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.021	0.001	0.001	0.000	1	1	0.972	0.990	0.001	0.000	19.182	4.891	See	See
32	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	4.776	3.688	See	See
33	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.726	0.000	0.022	0.000	-1	5	NaN	NaN	0.484	0.000	1.500	0.000	See	See
34	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.027	0.001	0.001	0.000	-1	5	0.985	0.977	0.001	0.000	36.529	11.912	See	See
35	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	5	1.000	1.000	0.000	0.000	19.421	15.326	See	See

KMeans_tall vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	3
max_iter	30
n_init	1
tol	0.0

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_tall	fit	1000000	1000000	2	30	0.564	0.0	0.851	0.000	random	NaN	30	NaN	0.398	0.0	1.418	0.000	See	See
1	KMeans_tall	predict	1000000	1000	2	30	0.001	0.0	0.408	0.000	random	0.001	30	0.001	0.000	0.0	7.846	4.634	See	See
2	KMeans_tall	predict	1000000	1	2	30	0.001	0.0	0.000	0.001	random	1.000	30	1.000	0.000	0.0	9.712	7.018	See	See
3	KMeans_tall	fit	1000000	1000000	2	30	0.530	0.0	0.906	0.000	k-means++	NaN	30	NaN	0.409	0.0	1.294	0.000	See	See
4	KMeans_tall	predict	1000000	1000	2	30	0.001	0.0	0.413	0.000	k-means++	0.001	30	0.000	0.000	0.0	7.060	3.757	See	See
5	KMeans_tall	predict	1000000	1	2	30	0.001	0.0	0.000	0.001	k-means++	1.000	30	1.000	0.000	0.0	9.995	7.136	See	See
6	KMeans_tall	fit	1000000	1000000	100	30	6.010	0.0	3.993	0.000	random	NaN	30	NaN	2.717	0.0	2.212	0.000	See	See
7	KMeans_tall	predict	1000000	1000	100	30	0.002	0.0	15.197	0.000	random	0.002	30	0.002	0.000	0.0	5.683	2.235	See	See
8	KMeans_tall	predict	1000000	1	100	30	0.001	0.0	0.019	0.001	random	1.000	30	1.000	0.000	0.0	9.215	5.548	See	See
9	KMeans_tall	fit	1000000	1000000	100	30	6.024	0.0	3.984	0.000	k-means++	NaN	30	NaN	2.841	0.0	2.120	0.000	See	See
10	KMeans_tall	predict	1000000	1000	100	30	0.002	0.0	15.000	0.000	k-means++	0.001	30	0.002	0.000	0.0	5.744	2.455	See	See
11	KMeans_tall	predict	1000000	1	100	30	0.001	0.0	0.020	0.001	k-means++	1.000	30	1.000	0.000	0.0	9.541	6.171	See	See

KMeans_short vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	300
max_iter	20
n_init	1
tol	0.0

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_short	fit	10000	10000	2	20	0.230	0.000	0.014	0.000	k-means++	NaN	20	NaN	0.086	0.0	2.674	0.000	See	See
1	KMeans_short	predict	10000	1000	2	20	0.002	0.000	0.190	0.000	k-means++	0.002	20	-0.000	0.001	0.0	2.622	0.493	See	See
2	KMeans_short	predict	10000	1	2	20	0.001	0.000	0.000	0.001	k-means++	1.000	20	1.000	0.000	0.0	10.158	7.072	See	See
3	KMeans_short	fit	10000	10000	2	20	0.077	0.000	0.041	0.000	random	NaN	20	NaN	0.031	0.0	2.469	0.000	See	See
4	KMeans_short	predict	10000	1000	2	20	0.002	0.000	0.179	0.000	random	0.001	20	0.001	0.001	0.0	2.721	0.617	See	See
5	KMeans_short	predict	10000	1	2	20	0.001	0.000	0.000	0.001	random	1.000	20	1.000	0.000	0.0	9.831	6.855	See	See
6	KMeans_short	fit	10000	10000	100	20	0.588	0.000	0.272	0.000	k-means++	NaN	20	NaN	0.359	0.0	1.636	0.000	See	See
7	KMeans_short	predict	10000	1000	100	20	0.003	0.001	4.847	0.000	k-means++	0.321	20	0.301	0.001	0.0	2.985	0.846	See	See
8	KMeans_short	predict	10000	1	100	20	0.001	0.000	0.013	0.001	k-means++	1.000	20	1.000	0.000	0.0	6.524	3.541	See	See
9	KMeans_short	fit	10000	10000	100	20	0.204	0.000	0.783	0.000	random	NaN	20	NaN	0.135	0.0	1.516	0.000	See	See
10	KMeans_short	predict	10000	1000	100	20	0.002	0.000	7.046	0.000	random	0.290	20	0.346	0.001	0.0	1.856	0.312	See	See
11	KMeans_short	predict	10000	1	100	20	0.001	0.000	0.013	0.001	random	1.000	20	1.000	0.000	0.0	6.842	3.639	See	See

LogisticRegression vs daal4py

All estimators share the following hyperparameters:

	value
penalty	l2
dual	False
tol	0.0001
C	1.0
fit_intercept	True
intercept_scaling	1
class_weight	NaN
random_state	NaN
solver	lbfgs
max_iter	100
multi_class	auto
verbose	0
warm_start	False
n_jobs	NaN
l1_ratio	NaN

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	LogisticRegression	fit	1000000	1000000	100	[20]	10.158	0.0	[-0.11614659]	0.000	NaN	NaN	NaN	NaN	NaN	1.793	0.0	5.666	0.000	See	See
1	LogisticRegression	predict	1000000	1000	100	[20]	0.000	0.0	[52.59448598]	0.000	NaN	NaN	NaN	NaN	0.543	0.000	0.0	0.889	0.482	See	See
2	LogisticRegression	predict	1000000	1	100	[20]	0.000	0.0	[0.22393031]	0.000	NaN	NaN	NaN	NaN	0.000	0.000	0.0	0.436	0.368	See	See
3	LogisticRegression	fit	1000	1000	10000	[26]	0.746	0.0	[2.78722331]	0.001	NaN	NaN	NaN	NaN	NaN	0.680	0.0	1.097	0.000	See	See
4	LogisticRegression	predict	1000	100	10000	[26]	0.002	0.0	[129.27821548]	0.000	NaN	NaN	NaN	NaN	0.330	0.003	0.0	0.571	0.114	See	See
5	LogisticRegression	predict	1000	1	10000	[26]	0.000	0.0	[20.78100614]	0.000	NaN	NaN	NaN	NaN	0.000	0.001	0.0	0.153	0.104	See	See

Ridge vs daal4py

All estimators share the following hyperparameters:

	value
alpha	1.0
fit_intercept	True
normalize	deprecated
copy_X	True
max_iter	NaN
tol	0.001
solver	auto
random_state	NaN

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	Ridge	fit	1000	1000	10000	NaN	0.177	0.000	0.451	0.0	NaN	NaN	NaN	0.181	0.0	0.980	0.000	See	See
1	Ridge	predict	1000	1000	10000	NaN	0.010	0.000	8.055	0.0	NaN	NaN	0.098	0.017	0.0	0.593	0.013	See	See
2	Ridge	predict	1000	1	10000	NaN	0.000	0.000	1.191	0.0	NaN	NaN	NaN	0.000	0.0	0.652	0.614	See	See
3	Ridge	fit	1000000	1000000	100	NaN	1.365	0.000	0.586	0.0	NaN	NaN	NaN	0.231	0.0	5.908	0.000	See	See
4	Ridge	predict	1000000	1000	100	NaN	0.000	0.001	1.921	0.0	NaN	NaN	1.000	0.000	0.0	1.695	3.610	See	See
5	Ridge	predict	1000000	1	100	NaN	0.000	0.000	0.013	0.0	NaN	NaN	NaN	0.000	0.0	0.721	0.707	See	See

Environment information

{
  "system_info": {
    "python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.2",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.20.3",
    "scipy": "1.6.3",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": "3.4.2",
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}

reporting_hpo = ReportingHpo(files=[
    "results/benchmarking/sklearn_HistGradientBoostingClassifier.csv",
    "results/benchmarking/xgboost_XGBClassifier.csv",
    "results/benchmarking/lightgbm_LGBMClassifier.csv",
    "results/benchmarking/catboost_CatBoostClassifier.csv"
])
reporting_hpo.run()

Scatter

Permutated curves

25th percentile

75th percentile